DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility

Modeling Inverse Demand Function with Explainable Dual Neural Networks

Financial contagion has been widely recognized as a fundamental risk to the financial system. Particularly potent is price-mediated contagion, wherein forced liquidations by firms depress asset prices and propagate financial stress, enabling crises to proliferate across a broad spectrum of seemingly unrelated entities. Price impacts are currently modeled via exogenous inverse demand functions. However, in real-world scenarios, only the initial shocks and the final equilibrium asset prices are typically observable, leaving actual asset liquidations largely obscured. This missing data presents significant limitations to calibrating the existing models. To address these challenges, we introduce a novel dual neural network structure that operates in two sequential stages: the first neural network maps initial shocks to predicted asset liquidations, and the second network utilizes these liquidations to derive resultant equilibrium prices. This data-driven approach can capture both linear and non-linear forms without pre-specifying an analytical structure; furthermore, it functions effectively even in the absence of observable liquidation data. Experiments with simulated datasets demonstrate that our model can accurately predict equilibrium asset prices based solely on initial shocks, while revealing a strong alignment between predicted and true liquidations. Our explainable framework contributes to the understanding and modeling of price-mediated contagion and provides valuable insights for financial authorities to construct effective stress tests and regulatory policies.Comment: Under Revie

Masked Imitation Learning: Discovering Environment-Invariant Modalities in Multimodal Demonstrations

Multimodal demonstrations provide robots with an abundance of information to make sense of the world. However, such abundance may not always lead to good performance when it comes to learning sensorimotor control policies from human demonstrations. Extraneous data modalities can lead to state over-specification, where the state contains modalities that are not only useless for decision-making but also can change data distribution across environments. State over-specification leads to issues such as the learned policy not generalizing outside of the training data distribution. In this work, we propose Masked Imitation Learning (MIL) to address state over-specification by selectively using informative modalities. Specifically, we design a masked policy network with a binary mask to block certain modalities. We develop a bi-level optimization algorithm that learns this mask to accurately filter over-specified modalities. We demonstrate empirically that MIL outperforms baseline algorithms in simulated domains including MuJoCo and a robot arm environment using the Robomimic dataset, and effectively recovers the environment-invariant modalities on a multimodal dataset collected on a real robot. Our project website presents supplemental details and videos of our results at: https://tinyurl.com/masked-ilComment: 13 page

The Stochastic Loss of Spikes in Spiking Neural P Systems: Design and Implementation of Reliable Arithmetic Circuits

Spiking neural P systems (in short, SN P systems) have been introduced as computing devices inspired by the structure and functioning of neural cells. The presence of unreliable components in SN P systems can be considered in many di erent aspects. In this paper we focus on two types of unreliability: the stochastic delays of the spiking rules and the stochastic loss of spikes. We propose the implementation of elementary SN P systems with DRAM-based CMOS circuits that are able to cope with these two forms of unreliability in an e cient way. The constructed bio-inspired circuits can be used to encode basic arithmetic modules

A model to predict the thermodynamic stability of abiotic methane-hydrogen binary hydrates in a marine serpentinization environment

Abiotic methane (CH4) and hydrogen (H2), which are produced during marine serpentinization, provide abundant gas source for hydrate formation on ocean floor. However, previous models of CH4–H2 hydrate formation have generally focused on pure water environments and have not considered the effects of salinity. In this study, the van der Waals–Platteeuw model, which considered the effects of salinity on the chemical potentials of CH4, H2, and H2O, was applied in a marine serpentinization environment. The model uses an empirical formula and the Peng–Robinson equation of state to calculate the Langmuir constants and fugacity values, respectively, of CH4 and H2, and it uses the Pitzer model to calculate the activity coefficients of H2O in the CH4–H2–seawater system. The three-phase equilibrium temperature and pressure predicted by the model for CH4–H2 hydrates in pure water demonstrated good agreement with experimental data. The model was then used to predict the three-phase equilibrium temperature and pressure for CH4–H2 hydrates in a NaCl solutions, for which relevant experimental data are lacking. Thus, this study provides a theoretical basis for gas hydrate research and investigation in areas with marine serpentinization

Spatiotemporal variations of tidal flat landscape patterns and driving forces in the Yangtze River Delta, China

As a crucial coastal wetland habitat in the transition zone between land and sea, global tidal flats have severely declined by 16% over the last two decades under the dual threats of intense human activities and climate change. The Yangtze River Delta of China, the largest estuary in the western Pacific Ocean, has abundant mudflat resources and a dense human population. It also has some of the most prominent conflicts between economic development and ecological conservation. The current lack of understanding of landscape patterns and influencing factors of the Yangtze River Delta mudflats has severely hampered the region’s ecological conservation and restoration efforts. Based on Landsat time-series images, this study generated a 30-m spatial resolution map of mudflats in the Yangtze River Delta, which shrank by 47% during 1990–2020, with a higher density of mudflat loss in Yancheng and Nantong cities of the Jiangsu province and Hangzhou, Shaoxing, and Ningbo cities of the Zhejiang province. Landscape indices, such as the patch density of tidal flats, have gradually changed since 2000, with most of them showing significant changes in 2010. Mudflats in Lianyungang, northwestern Yancheng, Nanhui, Jiaxing, and Hangzhou showed sharp negative changes in landscape characteristics. Natural and anthropogenic factors had synergistic effects on the above changes in mudflat landscape patterns in the Yangtze River Delta. Mudflat landscape features were mainly influenced by population growth, economic development, reclamation, sediment discharge, and air temperature. Based on the evolving characteristics of mudflat landscape patterns, we recommend improving mudflat landscape management and planning by strengthening mudflat policies, laws, and regulations, developing countermeasures against threats from major stressors, and enhancing the effectiveness of nature reserves for mudflat protection

SeroTracker-RoB: a decision rule-based algorithm for reproducible risk of bias assessment of seroprevalence studies

Risk of bias (RoB) assessments are a core element of evidence synthesis but can be time consuming and subjective. We aimed to develop a decision rule-based algorithm for RoB assessment of seroprevalence studies. We developed the SeroTracker-RoB algorithm. The algorithm derives seven objective and two subjective critical appraisal items from the Joanna Briggs Institute Critical Appraisal Checklist for Prevalence studies and implements decision rules that determine study risk of bias based on the items. Decision rules were validated using the SeroTracker seroprevalence study database, which included non-algorithmic RoB judgments from two reviewers. We quantified efficiency as the mean difference in time for the algorithmic and non-algorithmic assessments of 80 randomly selected articles, coverage as the proportion of studies where the decision rules yielded an assessment, and reliability using intraclass correlations comparing algorithmic and non-algorithmic assessments for 2070 articles. A set of decision rules with 61 branches was developed using responses to the nine critical appraisal items. The algorithmic approach was faster than non-algorithmic assessment (mean reduction 2.32 min [SD 1.09] per article), classified 100% (n = 2070) of studies, and had good reliability compared to non-algorithmic assessment (ICC 0.77, 95% CI 0.74–0.80). We built the SeroTracker-RoB Excel Tool, which embeds this algorithm for use by other researchers. The SeroTracker-RoB decision-rule based algorithm was faster than non-algorithmic assessment with complete coverage and good reliability. This algorithm enabled rapid, transparent, and reproducible RoB evaluations of seroprevalence studies and may support evidence synthesis efforts during future disease outbreaks. This decision rule-based approach could be applied to other types of prevalence studies